Multi-media method for enhanced recall and retention of educational of educational material

ABSTRACT

A method for enhancing recall and retention of educational material includes sending a series of structured interactions that are derived from educational material to a learner in small, discrete chunks over a period of time. The interactions may be repetitive and may require learner input and interaction.

CROSS-REFERENCES TO RELATED APPLICATIONS

This application claims the benefit of U.S. patent application Ser. No.15/454,433, filed by the same inventor on Mar. 9, 2017, now pending,which claimed the benefit of U.S. provisional patent application Ser.No. 62/305,747, filed by the same inventor on Mar. 9, 2016.

BACKGROUND

Improving educational and training outcomes has been an ongoingchallenge for broad segments of society. One of the critical factorsaffecting educational and training outcomes is the ability of thelearner to retain and recall significant aspects of the content overtime after a learning engagement such as a lecture, discussion orreading is completed.

Psychologists, educators, neuroscientists and other researchers whostudy learning have uncovered a multiplicity of factors that cansignificantly impact and improve retention and recall of any educationaland training content. These insights have had a rather limited impact onhow education and training are conducted, because implementing them hasnot been easy. The essential factors enhancing content retention andrecall of information are priming, cognitive anchor-points,multi-sensory engagement, information chunking, forced recall, diverseengagement venues and time-spaced engagement.

Priming is an implicit memory effect in which exposure to one stimulusinfluences the response to another stimulus. For example, if a teachergives a class a simulated final exam at the beginning of a course,before students have been exposed to the course content, studentsperform significantly better on the actual final at the end of thecourse than they would if they had not been given the simulated finalexam. Scores on the actual final exam improve regardless of how thestudents performed on the simulated exam.

Cognitive Anchor Points are keywords or portions of educational contentthat because of their proximity or relevance to larger sections ofcourse content trigger a learner's memory of that course content.

Multi-Sensory Engagement involves two or more of the senses within thesame activity.

Information Chunking presents no more than seven pieces of informationrelated to a specific learning objective within a short span of time.

Forced Recall requires a learner to actively recall information ratherthat passively reviewing that information. For example, the learnerwould be prompted to recall and write down information learned from asource text instead of re-reading the source text.

Diverse Engagement Venues requires a learner to engage with studymaterial in different physical surroundings each time the material isstudied and reviewed.

Time-spaced Engagement requires a learner to study or review portions ofstudy material in many short time blocks spread over several days ratherthan attempting to absorb an entire topic in a single block of time.

SUMMARY

A preferred embodiment of the invention provides a method for enhancingretention and recall of educational content. Content is extracted fromeducational text, audio or video. Extracted content may be categorizedand supplemented with additional material drawn from libraries and theweb. Extracted content is rendered into learning snippets, which arebrief questions or statements designed to first teach content, then testand reinforce retention of learned material. Learning snippets aredelivered via push notification to smartphone, computer and other meanson a schedule that employs priming, cognitive anchor points,multi-sensory engagement, information chunking, forced recall, diverseengagement venues and time-spaced engagement to reinforce learning,retention and recall.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a schematic diagram showing the extraction of Content Snippetsfrom a source of informational content.

FIG. 2 is a schematic diagram showing the sequencing of Content Snippetsinto Learning Snippets and grouping of Learning Snippets into LearningChunks.

FIG. 3 shows an example of a source of informational content.

FIG. 4 shows Learning Snippets extracted from the content of FIG. 3.

FIG. 5 is a schematic diagram showing a Learning Snippet converted intoa series of multi-sensory interactions.

FIG. 6 shows alternate Learning Snippets extracted from the content ofFIG. 3.

FIG. 7 shows multi-sensory interactions derived from the LearningSnippets of FIG. 6.

FIG. 8 shows a sample schedule of interactions delivered during atime-spaced engagement.

DETAILED DESCRIPTION

In the Summary of the Invention above and in the Detailed Description ofthe Invention, and the claims below, and in the accompanying drawings,reference is made to particular features (including method steps) of theinvention. It is to be understood that the disclosure of the inventionin this specification includes all possible combinations of suchparticular features. For example, where a particular feature isdisclosed in the context of a particular aspect or embodiment of theinvention, or a particular claim, that feature can also be used, to theextent possible, in combination with and/or in the context of otherparticular aspects and embodiments of the invention, and in theinvention generally.

The term “comprises” and grammatical equivalents thereof are used hereinto mean that other components, ingredients, steps, etc. are optionallypresent. For example, an article “comprising” (or “which comprises”)components A, B, and C can consist of (i.e., contain only) components A,B, and C, or can contain not only components A, B, and C but also one ormore other components.

Where reference is made herein to a method comprising two or moredefined steps, the defined steps can be carried out in any order orsimultaneously (except where the context excludes that possibility), andthe method can include one or more other steps which are carried outbefore any of the defined steps, between two of the defined steps, orafter all the defined steps (except where the context excludes thatpossibility).

The term “at least” followed by a number is used herein to denote thestart of a range beginning with that number (which may be a range havingan upper limit or no upper limit, depending on the variable beingdefined). For example, “at least 1” means 1 or more than 1. The term “atmost” followed by a number is used herein to denote the end of a rangeending with that number (which may be a range having 1 or 0 as its lowerlimit, or a range having no lower limit, depending upon the variablebeing defined). For example, “at most 4” means 4 or less than 4, and “atmost 40%” means 40% or less than 40%. When, in this specification, arange is given as “(a first number) to (a second number)” or “(a firstnumber) (a second number),” this means a range whose lower limit is thefirst number and whose upper limit is the second number. For example, 25to 100 mm means a range whose lower limit is 25 mm, and whose upperlimit is 100 mm.

An embodiment of the invention extracts discrete elements of declarativeknowledge such as definitions, facts and vocabularies from educationalmaterial. FIG. 1 shows a schematic diagram of the content elementextraction process. Extraction of content 100 may be performed by ahuman reader or by a content extraction engine 102. Each extractedcontent element becomes a content snippet 104, 106, 108.

The content extraction engine 102 is a computer-based text processingand analysis system which uses Natural Language Processing (NLP)technologies to create content snippets. The NLP system of an embodimentof the invention comprises one or more dictionaries, a learning grammarand task-specific libraries. Additional items can be added to achievespecific results from the NLP system. As an example, an NLP system canbe used to determine the sentiment of a document by using a library thatcategorizes and classifies words according to their sentiment (i.e.‘hate’ is a negative sentiment).

Subject dictionaries are utilized for identifying subject and subsequenttopics and sub-topics. For example, dictionaries for physics containterms such as atom, standard model, quantum theory, and energy.Dictionaries for economics contain terms such as inflation, moneysupply, M1, and M2. Each definition in a subject dictionary is furthersegmented into sub-topics and ranked from basic concepts to advancedconcepts. For example, atom is a basic concept but a Feynman diagram isan advanced concept. The content extraction engine identifies contentsnippet topics by matching terms in a content snippet to terms in asubject dictionary.

A learning grammar is based on a set of trigger words and/or expressionsdefined according to one or more selected knowledge models. The learninggrammar comprises words or expressions that are most often used todescribe learning elements within a text. Exemplary words andexpressions are “An atom (topic keyword) is the smallest (adjective)unit of matter (topic keyword),” “means,” and “defined as.” In theseexamples the inventive system identifies a character string as asentence if it includes a topic keyword and an adjective or number, orother listed words such as “means” or “defined as.”

Content snippets including trigger words may be classified intocategories such as definition, fact, and example. Additionally, anynon-textual content such as images, web URLs or bar codes are identifiedwith reference to their place in the text and their possiblecaption/heading/label and made part of a content snippet.

A further embodiment of the invention may include a content augmentationsystem that processes topic, sub-topic and word summary output from thecontent extraction engine, then uses a search engine to find andretrieve similar or supplementary results from internal libraries orfrom open resources on the internet. Results collected by crawling theweb are parsed by the content extraction engine. Only results containingthe proper subset of topic keywords are displayed. Content can befurther refined by date and authority of publication. The contentaugmentation system may determine the educational appropriateness of aresource by LEXILE® educational level measurement scores or other suchmeasures to ascertain the educational level of the learner.

An exemplary taxonomy of scientific topics and sub-topics ordered frombroadest to narrowest is natural sciences, physics, atomic physics,structure of atom (advanced), structure of atom (intermediate), andstructure of atom (basic). Topic keywords for each sub-topic may beextracted from a subject dictionary. As an example, the occurrence ofkeywords atom, nucleus, electron, neutron and proton combined with theabsence of keywords quark, gluon and standard model would cause thesystem to classify the content as structure of atom (basic).

Published content may be extracted from commercially-available sourcessuch as books, magazine articles, webinars and podcasts. As an example,a learner who wishes to improve recall of an article in an industrypublication such as the Harvard Business Review may have the articleprocessed through the system and made available to the learner.

Content may also be extracted from audio and video content, which arefirst transcribed into text using speech to text technologies. Textsegments are demarcated by word and line position. Transcribed video andaudio text is demarcated by time stamps on the audio and video timeline.

As shown in FIG. 2, small sets of content snippets 104, 106, 108 areordered by a content sequencing engine 200 into learning snippets 204,206, 208, then grouped as learning chunks 214. Learning snippets are thebasic building blocks of the training programs created and delivered bythe invention. The content sequencing engine 200 is a computer equippedwith a natural language application. The content sequencing engine 200arranges content snippets in order of occurrence in a content source,then groups them into learning chunks. Typical learning chunks comprisefive plus or minus two learning snippets.

Since a typical young person's working memory can reliably retain nomore than seven digits, six letters, or five words, learning chunks bydefault contain no more than seven learning snippets per contentsubject. Working memory generally diminishes as a user ages, withtypical older adults retaining four or fewer learning snippets. Thedefault limit may be changed manually by the user or by modifications tocontent sequencing engine settings, allowing the user to limit thenumber of learning snippets in a learning chunk to a number that isefficient for the user. FIG. 4 provides an example of learning snippets204, 206, 208, 210, 212 extracted from content 100 as shown in FIG. 3.In an alternate embodiment a learner may create and select learningsnippets for storage in a library and subsequent grouping as learningchunks.

FIG. 5 shows a learning snippet 204 within a learning chunk processed byan interaction generator 400 to produce a series of interactions 402,404, 406 to be delivered to a learner. FIGS. 6 and 7 provide examples ofinteractions 402, 404, 406, 408, 410 generated from correspondinglearning snippets 204, 206, 208, 210, 214. Interactions enhance contentretention and recall of information by utilizing multi-sensoryengagement, priming and forced recall.

The interaction generator is a text manipulation system. Each word in alearning snippet is classified according to its position in the sourcetext, then classified as a topic keyword, a “stop word” such as is, a,an; or a part of speech such as a verb, noun, adjective, determiner,adverb, pronoun, preposition, conjunction or interjection.

The interaction generator produces interactions in several formats,including presentation, open-ended, rearrangement and fill-in. Thepresentation format 404 presents the unmodified text of a learningsnippet along with links to any reference material. The open-endedformat 408 relies on known NLP processing techniques to present simpleopen-ended questionnaires containing interrogatives related to a topickeyword, such as “What is an Atom (topic keyword)?” or “Define an Atom(topic keyword)?” The rearrangement format presents the user with arandom jumble of words derived from a learning snippet. The fill-informat 406 presents a sentence from which the system has removed certaintypes of words such as the topic keyword, two or more letter words thatdo not include the topic keyword, an adjective and a topic keyword. Anexample is “Atom (topic keyword) is the smallest part of an element.”

Three primary interactions types are priming, review and forced recall.Priming and forced recall interactions require a response from thelearner. Priming requires a learner to answer a set of questions basedon a collection of learning snippets before exposure to the learningsnippets. Forced Recall requires a varied response from the learner.Responses incorporate multi-sensory response and may include typing aword or a sentence, using a mouse or a hand to write a response, andusing the microphone to record a response. Review interactions presentthe learner with the content of a learning snippet in text, audio orvideo form.

The inventive system develops an engagement schedule by selecting theformats and types of interactions to be delivered to a learner, thendefining the order, frequency and modes of delivery. An engagementschedule is algorithmically developed taking into account learnerobjectives such as strength of recall and time period for recall (month,year, multi-year).

FIG. 8 provides a sample engagement schedule 600 for delivery ofinteractions. Interactions for each learning snippet are delivered in astructured sequence. A priming interaction is delivered as the firstinteraction. A review interaction is delivered as a second. Subsequentinteractions, comprising of priming, review and forced recall are spreadout over an extended period of time.

Interactions may be delivered to the learner via a multitude of modes,including but not limited to text message, email, and messagingapplications such as FACEBOOK® Messenger messaging application,WHATSAPP® messaging application, GOOGLE+® messaging application, SLACK®messaging application, and HIPCHAT® messaging application, and may bedelivered through internet-connected programmable digital computingdevices including but not limited to desktop computers, notebookcomputers, tablets, web-enabled television screens, smartphones andmobile devices including but not limited to wearables such as theGOOGLE® Glass mobile device and the APPLE® iWatch mobile device.

A learner initiates a training program by selecting a source ofeducational material for content extraction. In an alternate embodimentof the invention a learner may subscribe to a publication that hasalready processed its content with the invention and providesinteractions on demand. Once the desired content has been extracted andprepared for delivery the learner is primed by completing acomprehensive simulated final examination on the educational material.The learner is then asked to review examination results and score his orher responses. Self-scoring and self-evaluation improve informationretention and the learner's awareness and understanding of his or herown thought processes.

When a learner has completed the priming stage the system beginsdelivery of interactions 402, 404, 406, 408, 410 according to anengagement schedule 600. Each engagement session starts with aninvitation 402 to answer questions about an identified study topic. Ifthe learner responds to the invitation, interactions containing thecontent of two or more learning chunks comprising seven to fourteenlearning snippets are sent sequentially. An interaction may include ahyperlink to any reference material the learner wish to review.

A schedule of interactions may be distributed over a time period rangingfrom hours to months, depending on the objectives of the learner.Interactions are presented to the learner at random times to ensureforced recall of content and to require recall in a variety ofenvironments.

In another embodiment of the invention a learner may be asked todescribe his or her environment, such as but not limited to the dinnertable, a gym, or a park, immediately after responding to an interaction.In an alternate embodiment the learner may be asked to undertakeinteractions within a set time. A learner may be sent reminders if thelearner does not respond in a timely fashion. A learner may set Do NotDisturb time slots, during which no interactions are sent.

Interactions require a variety of responses. Responses may includecompleting blanks in a text segment with missing words, writing asummary of the content from memory, verbally describing the key elementsof a learning chunk, responding verbally with a microphone on amultimedia device, rearranging words in a jumbled sentence, and writinga response to an interaction on a piece of paper and transmitting animage of the written response via email or text message. In a furtherembodiment of the invention, the learner may at random intervals bepresented with one or more questionnaire(s) to assess the learner'sretention of content. Questionnaire responses may be evaluated toidentify areas of deficiency. Delivery of subsequent interactions maythen be altered to focus on those areas of deficiency. The systemcontinually evolves through the use of machine learning and morepertinent data.

By combining content libraries in the system with subject dictionariesand topic classifiers a learner may identify his or her learningobjectives, with the system then guiding the learner through theidentified subject matter. For example, if the goal is to learn aboutthe standard model in physics, the system would guide the learnerthrough all the content describing the standard model. A learner can beoffered the option of completing self-assessments to measure any gapbetween the learner's current state of knowledge and the learner'sgoals. The learner may then use the system to develop a learning programto close the knowledge gap.

The principles, embodiments, and modes of operation of the presentinvention have been set forth in the foregoing specification. Theembodiments disclosed herein should be interpreted as illustrating thepresent invention and not as restricting it. The foregoing disclosure isnot intended to limit the range of equivalent structure available to aperson of ordinary skill in the art in any way, but rather to expand therange of equivalent structures in ways not previously contemplated.Numerous variations and changes can be made to the foregoingillustrative embodiments without departing from the scope and spirit ofthe present invention.

I claim:
 1. A method for enhancing retention and recall of educationalcontent comprising: selecting a source of educational material;extracting content snippets from the source educational material;sequencing the content snippets into learning snippets with aprogrammable digital computing device; grouping learning snippets intoat least a first learning chunk with the programmable digital computingdevice, the first learning chunk comprising between three and sevenlearning snippets, the maximum number of learning snippets in the firstlearning chunk adjusted to a user's working memory capacity; producingwith the programmable digital computing device at least a firstinteraction containing learning snippets from within the first learningchunk; selecting a format and a type for the first interaction;developing an engagement schedule by defining an order of delivery, afrequency of delivery and a mode of delivery; and delivering the firstinteraction to a learner via the Internet, and prompting the learner torecall and write down information learned from the source.
 2. A methodfor enhancing retention and recall of educational content comprising:selecting a source of educational material; extracting content snippetsfrom the source educational material; sequencing the content snippetsinto learning snippets with a programmable digital computing device;grouping learning snippets into at least a first learning chunk with theprogrammable digital computing device, the first learning chunkcontaining at least three but not more than seven learning snippets, themaximum number of learning snippets in the first learning chunk adjustedto a user's working memory capacity; producing with the programmabledigital computing device at least a first interaction containinglearning snippets from within the first learning chunk, the firstinteraction having a format selected from the group consisting ofpresentation, open-ended, rearrangement and fill-in; selecting a formatand a type for the first interaction; selecting priming as the firstinteraction type; developing an engagement schedule by defining an orderof delivery, a frequency of delivery and a mode of delivery; deliveringthe first interaction to a learner via the Internet, the firstinteraction prompting a learner response, the learner response includingtyping at least a first word, writing at least a second word, andrecording at least a third word with a microphone; and prompting thelearner to respond to the first interaction and to describe thelearner's physical environment immediately after responding to the firstinteraction.